Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration
IMPLIED is an implication modeling method that treats fixed-rule implications as an initial guide while learning to infer and revise accepted and rejected action labels over time, and predicts human implications more accurately than the fixed rule approach and LLM baselines, approaching the performance of a human-label...
Qiping Zhang, Kate Candon, Debasmita Ghose et al.
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